{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# OS 库基本操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 获取当前工作路径"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\zhangjunhong\\\\python系列库\\\\Python报表自动化'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 获取一个文件夹下的所有文件名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['3月绩效-张明明.xlsx', '李旦3月绩效.xlsx', '王玥月-3月绩效.xlsx', '陈凯3月份绩效.xlsx']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.listdir('D:/Data-Science/share/data/test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对文件进行重命名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.rename('D:/Data-Science/share/data/test/test_old.xlsx'\n",
    "          ,'D:/Data-Science/share/data/test/test_new.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建一个文件夹"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.mkdir('D:/Data-Science/share/data/test11')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除一个文件夹"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.removedirs('D:/Data-Science/share/data/test11')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 删除一个文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.remove('D:/Data-Science/share/data/test/test_new.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 批量操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 批量读取一个文件下的多个文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3月绩效-张明明.xlsx读取完成！\n",
      "李旦3月绩效.xlsx读取完成！\n",
      "王玥月-3月绩效.xlsx读取完成！\n",
      "陈凯3月份绩效.xlsx读取完成！\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "name_list = os.listdir('D:/Data-Science/share/data/test')\n",
    "\n",
    "for i in name_list:\n",
    "    df = pd.read_excel(r'D:/Data-Science/share/data/test/' + i)\n",
    "    print('{}读取完成！'.format(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 批量创建文件夹"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1月创建完成！\n",
      "2月创建完成！\n",
      "3月创建完成！\n",
      "4月创建完成！\n",
      "5月创建完成！\n",
      "6月创建完成！\n",
      "7月创建完成！\n",
      "8月创建完成！\n",
      "9月创建完成！\n",
      "10月创建完成！\n",
      "11月创建完成！\n",
      "12月创建完成！\n"
     ]
    }
   ],
   "source": [
    "month_num = ['1月','2月','3月','4月','5月','6月','7月','8月','9月','10月','11月','12月']\n",
    "\n",
    "for i in month_num:\n",
    "    os.mkdir('D:/Data-Science/share/data/' + i)\n",
    "    print('{}创建完成！'.format(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 批量重命名文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "#获取指定文件下所有文件名\n",
    "old_name = os.listdir('D:/Data-Science/share/data/test')\n",
    "\n",
    "name = [\"张明明\",\"李旦\",\"王玥月\",\"陈凯\"]\n",
    "\n",
    "#遍历每一个姓名\n",
    "for n in name:\n",
    "    #遍历每一个旧文件名\n",
    "    for o in old_name:\n",
    "        #判断旧文件名中是否包含特定的姓名\n",
    "        #如果包含就进行重命名\n",
    "        if n in o:\n",
    "            os.rename('D:/Data-Science/share/data/test/' + o, 'D:/Data-Science/share/data/test/' + n +\"3月绩效.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 其他批量操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 批量合并多个文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>销量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>1481.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>1260.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>1208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>1199.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>1301.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2021-06-26</td>\n",
       "      <td>1297.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2021-06-27</td>\n",
       "      <td>1340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>1129.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2021-06-29</td>\n",
       "      <td>1272.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>1340.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           日期      销量\n",
       "0  2021-01-01  1481.0\n",
       "1  2021-01-02  1260.0\n",
       "2  2021-01-03  1208.0\n",
       "3  2021-01-04  1199.0\n",
       "4  2021-01-05  1301.0\n",
       "..        ...     ...\n",
       "25 2021-06-26  1297.0\n",
       "26 2021-06-27  1340.0\n",
       "27 2021-06-28  1129.0\n",
       "28 2021-06-29  1272.0\n",
       "29 2021-06-30  1340.0\n",
       "\n",
       "[181 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "#获取指定文件下所有文件名\n",
    "name_list = os.listdir('D:/Data-Science/share/data/sale_data')\n",
    "\n",
    "#创建一个相同结构的空DataFrame\n",
    "df_o = pd.DataFrame({'日期':[],'销量':[]})\n",
    "\n",
    "#遍历读取每一个文件\n",
    "for i in name_list:\n",
    "    df = pd.read_excel(r'D:/Data-Science/share/data/sale_data/' + i)\n",
    "    #进行纵向拼接\n",
    "    df_v = pd.concat([df_o,df])\n",
    "    #把拼接后的结果赋值给df_o\n",
    "    df_o = df_v\n",
    "\n",
    "df_o   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 将一份文件按照指定列拆分成多个文件"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>销量</th>\n",
       "      <th>月份</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>1481.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>1260.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>1208.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>1301.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2021-06-26</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2021-06-27</td>\n",
       "      <td>1340.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2021-06-29</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>1340.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           日期      销量  月份\n",
       "0  2021-01-01  1481.0   1\n",
       "1  2021-01-02  1260.0   1\n",
       "2  2021-01-03  1208.0   1\n",
       "3  2021-01-04  1199.0   1\n",
       "4  2021-01-05  1301.0   1\n",
       "..        ...     ...  ..\n",
       "25 2021-06-26  1297.0   6\n",
       "26 2021-06-27  1340.0   6\n",
       "27 2021-06-28  1129.0   6\n",
       "28 2021-06-29  1272.0   6\n",
       "29 2021-06-30  1340.0   6\n",
       "\n",
       "[181 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_o['月份'] = df_o['日期'].apply(lambda x:x.month)\n",
    "df_o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#生成一列新的月份列\n",
    "df_o['月份'] = df_o['日期'].apply(lambda x:x.month)\n",
    "\n",
    "#遍历每一个月份值\n",
    "for m in df_o['月份'].unique():\n",
    "    #将特定月份值的数据筛选出来\n",
    "    df_month = df_o[df_o['月份'] == m]\n",
    "    #将筛选出来的数据进行保存\n",
    "    df_month.to_csv(r'D:/Data-Science/share/data/split_data/' + str(m) + '月销售日报_拆分后.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日期</th>\n",
       "      <th>销量</th>\n",
       "      <th>月份</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-06-01</td>\n",
       "      <td>1481.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-06-02</td>\n",
       "      <td>1260.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-06-03</td>\n",
       "      <td>1208.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-06-04</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-06-05</td>\n",
       "      <td>1301.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-06-06</td>\n",
       "      <td>1439.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021-06-07</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021-06-08</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021-06-09</td>\n",
       "      <td>1128.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021-06-10</td>\n",
       "      <td>1339.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2021-06-11</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2021-06-12</td>\n",
       "      <td>1060.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2021-06-13</td>\n",
       "      <td>1340.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2021-06-14</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2021-06-15</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2021-06-16</td>\n",
       "      <td>1249.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2021-06-17</td>\n",
       "      <td>1160.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2021-06-18</td>\n",
       "      <td>1151.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2021-06-19</td>\n",
       "      <td>1199.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>1301.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2021-06-21</td>\n",
       "      <td>1439.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2021-06-22</td>\n",
       "      <td>1468.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>1462.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>1128.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2021-06-25</td>\n",
       "      <td>1339.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2021-06-26</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2021-06-27</td>\n",
       "      <td>1340.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2021-06-29</td>\n",
       "      <td>1272.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>1340.0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           日期      销量  月份\n",
       "0  2021-06-01  1481.0   6\n",
       "1  2021-06-02  1260.0   6\n",
       "2  2021-06-03  1208.0   6\n",
       "3  2021-06-04  1199.0   6\n",
       "4  2021-06-05  1301.0   6\n",
       "5  2021-06-06  1439.0   6\n",
       "6  2021-06-07  1468.0   6\n",
       "7  2021-06-08  1462.0   6\n",
       "8  2021-06-09  1128.0   6\n",
       "9  2021-06-10  1339.0   6\n",
       "10 2021-06-11  1297.0   6\n",
       "11 2021-06-12  1060.0   6\n",
       "12 2021-06-13  1340.0   6\n",
       "13 2021-06-14  1129.0   6\n",
       "14 2021-06-15  1272.0   6\n",
       "15 2021-06-16  1249.0   6\n",
       "16 2021-06-17  1160.0   6\n",
       "17 2021-06-18  1151.0   6\n",
       "18 2021-06-19  1199.0   6\n",
       "19 2021-06-20  1301.0   6\n",
       "20 2021-06-21  1439.0   6\n",
       "21 2021-06-22  1468.0   6\n",
       "22 2021-06-23  1462.0   6\n",
       "23 2021-06-24  1128.0   6\n",
       "24 2021-06-25  1339.0   6\n",
       "25 2021-06-26  1297.0   6\n",
       "26 2021-06-27  1340.0   6\n",
       "27 2021-06-28  1129.0   6\n",
       "28 2021-06-29  1272.0   6\n",
       "29 2021-06-30  1340.0   6"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_month"
   ]
  }
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